Abstract 12120: Cost-Effectiveness of Earlier Initiation of Sodium-Glucose Cotransporter 2 Inhibitors in Patients With a History of Heart Failure
Bibliographic record
Abstract
Background: Patients with heart failure (HF) are at increased risk after hospitalisations for HF (HHF). Timely initiation of medical therapy may reduce morbidity and mortality in these patients. The Dapagliflozin And Prevention of Adverse-outcomes in Heart Failure (DAPA-HF) trial demonstrated that dapagliflozin reduced the risk of worsening HF and mortality when added to guideline-directed medical therapy (GDMT). Whilst previous economic evaluations have demonstrated that dapagliflozin is cost-effective; timing of initiation has not been considered. Objective: This study assessed whether early addition of dapagliflozin (D+GDMT) was cost-effective compared to a 12-month delay. Methods: A published lifetime Markov model was utilised with health states defined by Kansas City Cardiomyopathy (KCCQ) score, type 2 diabetes status and incidence of HF events. We analysed the DAPA-HF trial to estimate the incidence of HF events using a negative binomial regression model, and parametric survival analysis for all-cause death. Direct healthcare costs (2019 GBP) and utility values were sourced from the published literature and DAPA-HF, respectively. We estimated lifetime costs and quality adjusted life years (QALYs) for early initiation of D+GDMT compared to a 12-month delay. Results: Standard therapy was associated with lifetime costs of £12,641 and 4.11 QALYs. Treatment with D+GDMT with 12 months delay was associated with total discounted lifetime costs and QALYs of £15,966 and 4.71 respectively. Early initiation was associated with lifetime cost and QALYS; £16,518 and 4.78 respectively. Consequently, early initiation was associated with an additional cost of £552 and QALY gains of 0.08 resulting in an incremental cost effectiveness ratio of £8,515. Conclusion: Early or late initiation of D+GDMT is cost-effective. However, earlier initiation is associated with rapid improvement in patient symptoms leading to 10% greater QALY gains at a very attractive ICER.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".